Canadians report high levels of comfort with AI technology, businesses must work with consumer trust, low levels of adoption

TORONTO – December 12, 2017 – Canadians report that they are comfortable using AI technology in areas like controlling their house temperature, utilities and appliances, scheduling and appointments, and shopping/eating recommendations, but less so in areas that affect their personal lives.

These findings are from the annual Canadian Artificial Intelligence Tracker, the first of its kind in Canada, which was conducted by the Research and Analytics Services team of Sklar Wilton & Associates, a leading Canadian marketing and strategic advisory consultancy.

While Canadians are comfortable using artificial intelligence to control their house temperature (73%), utilities and appliances (59%), scheduling and appointments (70%), and shopping/eating recommendations (59%), they are less comfortable in areas directly affecting their personal lives and well-being. Less than half of Canadians are comfortable with AI diagnosing their medical conditions without doctor involvement (43%) and even fewer are comfortable with AI driving vehicles without human involvement (39%).

While comfort levels vary among different areas of work and home life, adoption of AI at a personal level is still in its infancy. Just over one-in-ten people (12%) report that they already use AI technologies in their personal life and a similar proportion (11%) say that they use them at work. Trust is also precarious and rests highly on transparency from companies – more than three-quarters (78%) of Canadians say they absolutely require to know whether they are talking to a human being or a chatbot. Many consumers feel suspicious towards the companies that implement them, with four-in-ten (41%) stating that companies using AI are focused on reducing their costs at the expense of people.

“We are quite intrigued by these results,” said Charlie Wilton FMRIA, Partner at Sklar Wilton & Associates. “It’s clear that Canadians are eager to adopt AI technologies and they recognize these products and services can have amazing advantages. However, so far, they’re somewhat more skeptical when AI touches their personal lives, or could have impacts on their loved ones.”

What does this mean for marketing and business? Most businesses are gearing up to adopt and develop AI strategies into their products and services. However, if they want to be successful, they must put the consumer dimension at the forefront of their AI strategy. They will need to:

Foster Consumer-Driven Innovation: Shape the end-user experience in a positive way by satisfying specific needs rather than simply using AI for ‘upselling.’

Establish High Ethical Standards: Personalize content such that it is useful and impactful for consumers not simply a manipulation of human psychology. Rather than using AI technologies to maximize short-term gains leading to increased customer attrition, create AI technologies that help to build trust and foster long-term customer satisfaction and engagement.

Be Transparent and Honest in Communications: Be prepared to raise business standards and increase transparency around how AI technologies are used. Take advantage of AI capabilities to dramatically improve customer service and engagement.

The full report outlines Canadians expectations and comprehension of AI, their adoption and usage of AI tools, as well as how they feel about AI. It also outlines expectations of and implications for government, businesses, brands, and marketers. The report can be downloaded on the Sklar Wilton & Associates website.

Methodology

The Canadian Artificial Intelligence Tracker was conducted by Sklar Wilton & Associates among Canadians 18+ with data collected from July 31 to August 7, 2017. Participants were selected from among those who have volunteered to participate in online surveys. The data were weighted to reflect the demographic composition of adult Canadians. Estimates of sampling error cannot be calculated. All sample surveys are subject to error, including, but not limited to sampling error, coverage error, and measurement error.